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Scenario: Your organization stores customer data in an on-premises Apache Hadoop cluster in Apache Parquet format. Apache Spark jobs process the data daily on the cluster. You plan to migrate the Parquet data and Spark jobs to Google Cloud. Future transformation pipelines will use BigQuery, so the data must be accessible in BigQuery. You want to use managed services while minimizing changes to ETL processing and reducing overhead costs. Question: How should you migrate and manage the data and Spark pipelines to meet the requirements?